Mr. Razvan Ionasec Profile

Razvan Ionasec

at Siemens Corporate Research

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Publications (6)

Proceedings Article | 12 March 2013 Paper

Significant acceleration of 2D-3D registration-based fusion of ultrasound and x-ray images by mesh-based DRR rendering

Markus Kaiser, Matthias John, Anja Borsdorf, Peter Mountney, Razvan Ionasec, Alois Nöttling, Philipp Kiefer, Jörg Seeburger, Thomas Neumuth

Proceedings Volume 8671, 867111 (2013) https://doi.org/10.1117/12.2007440

KEYWORDS: Image registration, X-rays, X-ray imaging, Image fusion, Ultrasonography, 3D modeling, 3D image processing, Surgery, Heart, X-ray detectors

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Proceedings Article | 2 March 2011 Paper

Learning distance function for regression-based 4D pulmonary trunk model reconstruction estimated from sparse MRI data

Dime Vitanovski, Alexey Tsymbal, Razvan Ionasec, Bogdan Georgescu, Shaohua K. Zhou, Joachim Hornegger, Dorin Comaniciu

Proceedings Volume 7964, 79640L (2011) https://doi.org/10.1117/12.878195

KEYWORDS: Data modeling, Magnetic resonance imaging, 3D modeling, Heart, Computed tomography, Sensors, Data acquisition, Transparency, Surgery, 4D CT imaging

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Congenital heart defect (CHD) is the most common birth defect and a frequent cause of death for children. Tetralogy of Fallot (ToF) is the most often occurring CHD which affects in particular the pulmonary valve and trunk. Emerging interventional methods enable percutaneous pulmonary valve implantation, which constitute an alternative to open heart surgery. While minimal invasive methods become common practice, imaging and non-invasive assessment tools become crucial components in the clinical setting. Cardiac computed tomography (CT) and cardiac magnetic resonance imaging (cMRI) are techniques with complementary properties and ability to acquire multiple non-invasive and accurate scans required for advance evaluation and therapy planning. In contrary to CT which covers the full 4D information over the cardiac cycle, cMRI often acquires partial information, for example only one 3D scan of the whole heart in the end-diastolic phase and two 2D planes (long and short axes) over the whole cardiac cycle. The data acquired in this way is called sparse cMRI. In this paper, we propose a regression-based approach for the reconstruction of the full 4D pulmonary trunk model from sparse MRI. The reconstruction approach is based on learning a distance function between the sparse MRI which needs to be completed and the 4D CT data with the full information used as the training set. The distance is based on the intrinsic Random Forest similarity which is learnt for the corresponding regression problem of predicting coordinates of unseen mesh points. Extensive experiments performed on 80 cardiac CT and MR sequences demonstrated the average speed of 10 seconds and accuracy of 0.1053mm mean absolute error for the proposed approach. Using the case retrieval workflow and local nearest neighbour regression with the learnt distance function appears to be competitive with respect to "black box" regression with immediate prediction of coordinates, while providing transparency to the predictions made.

Proceedings Article | 13 March 2010 Paper

Learning discriminative distance functions for valve retrieval and improved decision support in valvular heart disease

Ingmar Voigt, Dime Vitanovski, Razvan Ionasec, Alexey Tsymal, Bogdan Georgescu, Shaohua Zhou, Martin Huber, Nassir Navab, Joachim Hornegger, Dorin Comaniciu

Proceedings Volume 7623, 762314 (2010) https://doi.org/10.1117/12.843972

KEYWORDS: Heart, Visualization, 3D modeling, Ultrasonography, Decision support systems, Motion models, Distance measurement, Feature extraction, Diagnostics, Computed tomography

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Proceedings Article | 24 February 2010 Paper

Aortic valve and ascending aortic root modeling from 3D and 3D+t CT

Saša Grbic, Razvan Ionasec, Dominik Zäuner, Yefeng Zheng, Bogdan Georgescu, Dorin Comaniciu

Proceedings Volume 7625, 76250H (2010) https://doi.org/10.1117/12.844566

KEYWORDS: 3D modeling, Data modeling, Principal component analysis, Sensors, Visualization, Distance measurement, Visual process modeling, 3D metrology, Heart, Optical tracking

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Proceedings Article | 27 March 2009 Paper

Shape-based diagnosis of the aortic valve

Razvan Ioan Ionasec, Alexey Tsymbal, Dime Vitanovski, Bogdan Georgescu, S. Kevin Zhou, Nassir Navab, Dorin Comaniciu

Proceedings Volume 7259, 725908 (2009) https://doi.org/10.1117/12.812488

KEYWORDS: 3D modeling, Data modeling, Computer aided diagnosis and therapy, Machine learning, Pathology, Computed tomography, Visualization, Diagnostics, Heart, 3D image processing

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Proceedings Article | 13 March 2009 Paper

Model-driven physiological assessment of the mitral valve from 4D TEE

Ingmar Voigt, Razvan Ioan Ionasec, Bogdan Georgescu, Helene Houle, Martin Huber, Joachim Hornegger, Dorin Comaniciu

Proceedings Volume 7261, 72610R (2009) https://doi.org/10.1117/12.812096

KEYWORDS: Data modeling, Ultrasonography, Visualization, Visual process modeling, Pathology, Motion estimation, Error analysis, Model-based design, Motion models, Heart

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Showing 5 of 6 publications

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